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Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for sea-land segmentation and the results could be further improved. This paper proposes a novel deep convolution neural network named DeepUNet. Like the U-Net, its structure has a contracting path and an expansive path to get high resolution output. But differently, the DeepUNet uses DownBlocks instead of convolution layers in the contracting path and uses UpBlock in the expansive path. The two novel blocks bring two new connections that are U-connection and Plus connection. They are promoted to get more precise segmentation results. To verify our network architecture, we made a new challenging sea-land dataset and compare the DeepUNet on it with the SegNet and the U-Net. Experimental results show that DeepUNet achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

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The first example of direct and site-selective arylation of indoles at the C6 position has been reported. The key to this high regioselectivity is the appropriate choice of the N-P(O)(t)Bu2 directing group and the use of diaryliodonium triflate salts as the coupling partners in the presence of catalytic CuO. The protocol is distinguished by mild reaction system that avoids ligand and additives, exhibiting wide scope of indole and arene coupling components without compromising its efficiency and scalability, thus representing a significant advancement in the implementation of regioselective direct arylation of indoles.